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Inspection system for bonded honeycomb assemblies is accurate, fast, and automated. The ultrasonic system consists of inner and outer transducer positioning assemblies with suitable motor controls, a centerless turntable assembly, water squirter assemblies, and an inspection program completely encoded on tape suitable for use on a high speed computer.
The Deep Space Optical Terminal (DOT) transmit module demonstrates the DOT downlink signaling in a flight electronics assembly that can be qualified for deep space. The assembly has the capability to generate an electronic pulse-position modulation (PPM) waveform suitable for driving a laser assembly to produce the optical downlink signal. The downlink data enters the assembly through a serializer/ deserializer (SERDES) interface, and is encoded using a serially concatenated PPM (SCPPM) forward error correction code. The encoded data is modulated using PPM with an inter-symbol guard time to aid in receiver synchronization. Monitor and control of the assembly is via a low-voltage differential signal (LVDS) interface
Relating the macroscopic properties of protein-based materials to their underlying component microstructure is an outstanding challenge. Here, we exploit computational design to specify the size, flexibility, and valency of de novo protein building blocks, as well as the interaction dynamics between them, to investigate how molecular parameters govern the macroscopic viscoelasticity of the resultant protein hydrogels. We construct gel systems from pairs of symmetric protein homo-oligomers, each comprising 2, 5, 24, or 120 individual protein components, that are crosslinked either physically or covalently into idealized step-growth biopolymer networks. Through rheological assessment, we find that the covalent linkage of multifunctional precursors yields hydrogels whose viscoelasticity depends on the crosslink length between the constituent building blocks. In contrast, reversibly crosslinking the homo-oligomeric components with a computationally designed heterodimer results in viscoelastic biomaterials exhibiting fluid-like properties under rest and low shear, but solid-like behavior at higher frequencies. Exploiting the unique genetic encodability of these materials, we demonstrate the assembly of protein networks within living mammalian cells and show via fluorescence recovery after photobleaching (FRAP) that mechanical properties can be tuned intracellularly in a manner similar to formulations formed extracellularly. We anticipate that the ability to modularly construct and systematically program the viscoelastic properties of designer protein-based materials could have broad utility in biomedicine, with applications in tissue engineering, therapeutic delivery, and synthetic biology.
This Phase 2 effort applies the results of Phase 1 to design and fabricate an optical slip ring system for a helicopter rotor blade/wind tunnel application. In this application, there are two assemblies: one on the rotating portion of the mechanical system, one on the stationary portion. The assembly on the rotating portion digitizes and encodes 128 transducer signals from various parts of the blade, and optically transfers data across the noncontacting coupling. Two complete identical independent channels are provided. On the stationary side, the signals are decoded and one channel is transmitted in digital form to a computer for recording and analysis. The second channel reconstructs the analog transducer signals for real time observation. In the opposite direction, eight signal channels enable control signals to be passed from the stationary to the rotating part of the system. Power to the rotor mounted electronics is supplied via power slip rings. The advantages of the optical over the traditional electro-mechanical slip ring method of data transfer across a rotating joint are long life, low-maintenance, immunity to crosstalk, and wider bandwidth. Successful completion of this effort demonstrated that this method is practical and reliable, and can be implemented under difficult conditions of available space, power, environment, and stringent performance and equipment life requirements.
The ability to fabricate materials and devices at small scales by design has resulted in tremendous technological progress. However, the need for engineered three-dimensional (3D) nanoscale materials requires new strategies for organizing nanocomponents. Here we demonstrate an inverse design approach for the assembly of nanoparticles into hierarchically ordered 3D organizations using DNA voxels with directional, addressable bonds. By identifying intrinsic symmetries in repeating mesoscale structural motifs, we prescribe a set of voxels, termed a mesovoxel, that are assembled into target 3D crystals. The relationship between different degrees of encoded information used for voxel bonds and the fidelity of assembly is investigated using experimental and computational methods. We apply this assembly strategy to create periodic 3D nanoparticle ordered organizations, including structures with low-dimensional elements, helical motifs, a nanoscale analogue of a face-centred perovskite crystal and a distributed Bragg reflector based on a crystal with plasmonic and photonic length-scale regimes.
DNA-based self-assembly methods have demonstrated powerful and unique capabilities to encode nanomaterial structures through the prescribed placement of inorganic and biological nanocomponents. However, the challenge of selectively growing DNA superlattices on specific locations of surfaces and their integration with conventional nanofabrication has hindered the fabrication of three-dimensional (3D) DNA-assembled functional devices. Here, we present a scalable nanofabrication technique that combines bottom-up and top-down approaches for selective growth of 3D DNA superlattices on gold microarrays. This approach allows for the fabrication of self-assembled 3D-nanostructured electronic devices. DNA strands are bound onto the gold arrays, which anchor DNA origami frames and promote ordered framework growth on the specific areas of the surface, enabling control of the lateral placement and orientation of superlattices. DNA frameworks selectively grown on the pads are subsequently templated to nanoscale silica and tin oxide (SnO x ) that follow the architecture, as confirmed by structural and chemical characterizations. The fabricated SnO x superlattices are integrated into devices that demonstrate photocurrent response.
Abstract Large-genome bacteriophages (jumbo phages) of the proposed family Chimalliviridae assemble a nucleus-like compartment bounded by a protein shell that protects the replicating phage genome from host-encoded restriction enzymes and DNA-targeting CRISPR-Cas nucleases. While the nuclear shell provides broad protection against host nucleases, it necessitates transport of mRNA out of the nucleus-like compartment for translation by host ribosomes, and transport of specific proteins into the nucleus-like compartment to support DNA replication and mRNA transcription. Here, we identify a conserved phage nuclear shell-associated protein that we term Chimallin C (ChmC), which adopts a nucleic acid-binding fold, binds RNA with high affinity in vitro, and binds phage mRNAs in infected cells. ChmC also forms phase-separated condensates with RNA in vitro. Targeted knockdown of ChmC using mRNA-targeting dCas13d results in accumulation of phage-encoded mRNAs in the phage nucleus, reduces phage protein production, and compromises virion assembly. Taken together, our data show that the conserved ChmC protein plays crucial roles in the viral life cycle, potentially by facilitating phage mRNA translocation through the nuclear shell to promote protein production and virion development.
Neural plasticity, the ability to alter the structure and function of neural circuits, varies throughout the age of an individual. The end of the hyperplastic period in the central nervous system coincides with the appearance of honeycomb-like structures called perineuronal nets (PNNs) that surround a subset of neurons. PNNs are a condensed form of neural extracellular matrix that include the glycosaminoglycan hyaluronan and extracellular matrix proteins such as aggrecan and tenascin-R (TNR). PNNs are key regulators of developmental neural plasticity and cognitive functions, yet our current understanding of the molecular interactions that help assemble them remains limited. Disruption of Ptprz1, the gene encoding the receptor protein tyrosine phosphatase RPTPζ, altered the appearance of nets from a reticulated structure to puncta on the surface of cortical neuron bodies in adult mice. The structural alterations mirror those found in Tnr -/- mice, and TNR is absent from the net structures that form in dissociated cultures of Ptprz1 -/- cortical neurons. These findings raised the possibility that TNR and RPTPζ cooperate to promote the assembly of PNNs. Here, we show that TNR associates with the RPTPζ ectodomain and provide a structural basis for these interactions. Furthermore, we show that RPTPζ forms an identical complex with tenascin-C, a homolog of TNR that also regulates neural plasticity. Finally, we demonstrate that mutating residues at the RPTPζ–TNR interface impairs the formation of PNNs in dissociated neuronal cultures. Overall, this work sets the stage for analyzing the roles of protein–protein interactions that underpin the formation of nets.
Plasmids are mobile genetic elements that play a key role in microbial ecology and evolution by mediating horizontal transfer of important genes, such as antimicrobial resistance genes. Many microbial genomes have been sequenced by short read sequencers and have resulted in a mix of contigs that derive from plasmids or chromosomes. New tools that accurately identify plasmids are needed to elucidate new plasmid-borne genes of high biological importance. We have developed Deeplasmid, a deep learning tool for distinguishing plasmids from bacterial chromosomes based on the DNA sequence and its encoded biological data. It requires as input only assembled sequences generated by any sequencing platform and assembly algorithm and its runtime scales linearly with the number of assembled sequences. Deeplasmid achieves an AUC–ROC of over 89%, and it was more accurate than five other plasmid classification methods. Finally, as a proof of concept, we used Deeplasmid to predict new plasmids in the fish pathogen Yersinia ruckeri ATCC 29473 that has no annotated plasmids. Deeplasmid predicted with high reliability that a long assembled contig is part of a plasmid. Using long read sequencing we indeed validated the existence of a 102 kb long plasmid, demonstrating Deeplasmid's ability to detect novel plasmids.
CRISPR interference (CRISPRi) has emerged as a valuable tool for redirecting metabolic flux to enhance bioproduction. However, its application is often constrained by two challenges: (i) rationally identifying effective gene targets for downregulation and (ii) efficiently constructing multiplexed CRISPRi systems. In this study, we address both challenges by integrating a computational prioritization tool with a versatile assembly method for building multiplexed CRISPRi systems. FluxRETAP (Flux-Reaction Target Prioritization) accurately identified gene targets whose knockdown led to substantial increase of isoprenol titers in Pseudomonas putida KT2440, outperforming a conventional non-computational, pathway-guided target selection. The highest isoprenol titer of nearly 1.5 g/L was achieved by knocking down PP_4118 (a gene encoding α-ketoglutarate dehydrogenase). The use of VAMMPIRE (Versatile Assembly Method for MultiPlexing CRISPRi-mediated downREgulation) enabled accurate assembly of CRISPRi constructs containing up to five sgRNA arrays, reducing context dependency and achieving uniform, position-independent gene downregulation. The integration of FluxRETAP and VAMMPIRE has the potential to advance metabolic engineering by rapidly identifying CRISPRi-mediated knockdowns and knockdown combinations that enhance bioproduction titers, with potential applicability to other microbial systems.
Botulinum neurotoxins (BoNTs) are among the most lethal toxins known to humans, comprising seven established serotypes termed BoNT/A–G encoded in two types of gene clusters (ha and orfX) in BoNT-producing clostridia. The ha cluster encodes four non-toxic neurotoxin-associated proteins (NAPs) that assemble with BoNTs to protect and enhance their oral toxicity. However, the structure and function of the orfX-type NAPs remain largely unknown. Here, we report the crystal structures for OrfX1, OrfX2, and an OrfX1–OrfX3 complex, which are encoded in the orfX cluster of a BoNT/E1-producing Clostridium botulinum strain associated with human foodborne botulism. These structures lay the foundation for future studies on the potential roles of OrfX proteins in oral intoxication and pathogenesis of BoNTs.
Synthetic oligonucleotides and peptides have enjoyed a wide range of applications in both biology and chemistry. As a consequence, oligonucleotide-peptide conjugates have received considerable attention, most notably in the development of antisense constructs with improved pharmacological properties. In addition, oligonucleotide-peptide conjugates have been used as molecular tags, in the assembly of supramolecular arrays and in the construction of encoded combinatorial libraries. To make these chimeric molecules more accessible for a broad range of investigations, we sought to develop a facile method for joining fully deprotected oligonucleotides and peptides through a stable amide bond linkage. Furthermore, we wished to make this ligation reaction addressable, enabling one to direct the ligation of specific oligonucleotide and peptide components.To confer specificity and accelerate the rate of the reaction, the ligation process was designed to be dependent on the presence of a complementary oligonucleotide template.
In evolutionary algorithms a common method for encoding neural networks is to use a tree structured assembly procedure for constructing them. Since node operators have difficulties in specifying edge weights and these operators are execution-order dependent, an alternative is to use edge operators. Here we identify three problems with edge operators: in the initialization phase most randomly created genotypes produce an incorrect number of inputs and outputs; variation operators can easily change the number of input/output (I/O) units; and units have a connectivity bias based on their order of creation. Instead of creating I/O nodes as part of the construction process we propose using parameterized operators to connect to preexisting I/O units. Results from experiments show that these parameterized operators greatly improve the probability of creating and maintaining networks with the correct number of I/O units, remove the connectivity bias with I/O units and produce better controllers for a goal-scoring task.
Rubisco catalyses the first step in carbon fixation and is a strategic target to improve photosynthetic efficiency. In plants, Rubisco is composed of eight large and eight small subunits and its biogenesis requires multiple chaperones. We optimised a system to produce tobacco Rubisco in Escherichia coli by co-expressing chaperones in auto-induction medium. We successfully assembled tobacco Rubisco in E. coli with each small subunit that is normally encoded by the nuclear genome. Even though each enzyme carries only a single type of small subunit in E. coli, the enzymes exhibit carboxylation kinetics very similar to that of the native Rubisco. Tobacco Rubisco assembled with a recently discovered trichome small subunit has a higher catalytic rate and a lower CO 2 affinity than those assembled with other small subunits. Finally, our E. coli expression system will allow probing of features of both subunits of Rubisco that affect its kinetic properties.
Future space systems will require control sensors capable of real-time measurements of position coordinates of many structural locations. Applications for such a sensor include figure and vibration control, rendezvous and docking, and structure assembly verification. The paper discusses an experimental study of SHAPES (spatial, high-accuracy, position-encoding sensor), a 3D position sensor that provides range and two angular positions of laser-illuminated retroreflector targets that mark the locations to be measured. Simultaneous range measurements to multiple targets by a time-of-flight corelation of short laser pulses are made with a CCD-equipped streak tube. Angular positions are measured with a CCD camera. Position measurements of 24 targets with sub-millimeter range accuracy at a 10 Hz update rate have been demonstrated.
Having the ability to predict the protein-encoding gene content of an incomplete genome or metagenome-assembled genome is important for a variety of bioinformatic tasks. In this study, as a proof of concept, we built machine learning classifiers for predicting variable gene content in Escherichia coli genomes using only the nucleotide k-mers from a set of 100 conserved genes as features. Protein families were used to define orthologs, and a single classifier was built for predicting the presence or absence of each protein family occurring in 10%–90% of all E. coli genomes. The resulting set of 3,259 extreme gradient boosting classifiers had a per-genome average macro F1 score of 0.944 [0.943–0.945, 95% CI]. We show that the F1 scores are stable across multi-locus sequence types and that the trend can be recapitulated by sampling a smaller number of core genes or diverse input genomes. Surprisingly, the presence or absence of poorly annotated proteins, including “hypothetical proteins” was accurately predicted (F1 = 0.902 [0.898–0.906, 95% CI]). Models for proteins with horizontal gene transfer-related functions had slightly lower F1 scores but were still accurate (F1s = 0.895, 0.872, 0.824, and 0.841 for transposon, phage, plasmid, and antimicrobial resistance-related functions, respectively). Finally, using a holdout set of 419 diverse E. coli genomes that were isolated from freshwater environmental sources, we observed an average per-genome F1 score of 0.880 [0.876–0.883, 95% CI], demonstrating the extensibility of the models. Overall, this study provides a framework for predicting variable gene content using a limited amount of input sequence data.
The goal of this project was to explore the effect of altering the carbon fixing enzyme Rubisco in the model C3 plant tobacco on photosynthesis and plant growth and development. We introduced the gene sequences encoding Rubisco from a Limonium species and from a red alga into tobacco and found that the enzyme did not assemble properly, undoubtedly requiring additional assembly factors from the source organism. We then developed a method to make single amino-acid changes into the Rubisco subunit that is encoded by the chloroplast genome. We used the method to investigate the role of particular amino acid residues in kinetic properties of Rubisco. Finally, we improved a bacterial expression system that has allowed assembly of active tobacco Rubisco in E. coli. We used the improved system to investigate the kinetic properties of Rubisco enzymes that contain only one type of Rubisco small subunit along with the large subunit.
Tree codes are known to be capable of performing arbitrarily close to the rate-distortion function for any memoryless source and single-letter fidelity criterion. Tree coding and tree search strategies are investigated for the discrete-time memoryless Gaussian source encoded for a signal-power-to-mean-squared-error ratio of about 30 dB (about 5 binary digits per source output). Also, a theoretical lower bound on average search effort is derived. Two code search strategies (the Viterbi algorithm and the stack algorithm) were simulated in assembly language on a large digital computer. After suitable modifications, both strategies yielded encoding with a signal-to-distortion ratio about 1 dB below the limit set by the rate-distortion function. Although this performance is better than that of any previously known instrumentable scheme, it unfortunately requires search computation of the order of 100,000 machine cycles per source output encoded.